Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 Jan;89(1):31-35.
doi: 10.1177/00031348221109478. Epub 2022 Jun 18.

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

Abbas M Hassan  1 , Andrea Biaggi-Ondina  1 , Aashish Rajesh  2 , Malke Asaad  3 , Jonas A Nelson  4 , J Henk Coert  5 , Babak J Mehrara  4 , Charles E Butler  1
Affiliations
Review

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

Abbas M Hassan et al. Am Surg. 2023 Jan.

Abstract

Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.

Keywords: artificial intelligence; deep learning; machine learning; patient-reported outcomes; surgery.

PubMed Disclaimer

Conflict of interest statement

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Butler is a consultant for Allergan Inc.

References

    1. McCormick JD, Werner BC, Shimer AL. Patient-reported outcome measures in spine surgery. J Am Acad Orthop Surg. 2013;21(2):99–107. - PubMed
    1. Waljee J, McGlinn EP, Sears ED, Chung KC. Patient expectations and patient-reported outcomes in surgery: A systematic review. Surgery. 2014;155(5):799–808. - PMC - PubMed
    1. Black N Patient reported outcome measures could help transform healthcare. BMJ. 2013;346:f167. - PubMed
    1. Pusic AL, Lemaine V, Klassen AF, Scott AM, Cano SJ. Patient-reported outcome measures in plastic surgery: Use and interpretation in evidence-based medicine. Plast Reconstr Surg. 2011;127(3):1361–1367. - PubMed
    1. Hassan A, Lu S, Asaad M, et al. Novel machine learning approach for prediction of hernia recurrence, surgical complications, and 30-day readmission following abdominal wall reconstruction. J Am Coll Surg. 2022;234(5): 918–927. - PubMed

LinkOut - more resources